
"""simple docstring"""





import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__magic_name__			=     logging.get_logger(__name__)

__magic_name__			=     {"""vocab_file""": """spiece.model"""}

__magic_name__			=     {
    """vocab_file""": {
        """bert_for_seq_generation""": (
            """https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model"""
        ),
    }
}

__magic_name__			=     {"""bert_for_seq_generation""": 512}
class 		SCREAMING_SNAKE_CASE_					(		a_					):




		"""simple docstring"""





		__lowercase				:    List[str]		  =						VOCAB_FILES_NAMES
		__lowercase				:    str		  =						PRETRAINED_VOCAB_FILES_MAP
		__lowercase				:    Any		  =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__lowercase				:    Dict		  =						[]
		__lowercase				:    Tuple		  =						['''input_ids''', '''attention_mask''']




		def __init__(	self				,    lowerCAmelCase__				,    lowerCAmelCase__="<s>"				,    lowerCAmelCase__="</s>"				,    lowerCAmelCase__="<unk>"				,    lowerCAmelCase__="<pad>"				,    lowerCAmelCase__="<::::>"				,    lowerCAmelCase__ = None				,    **lowerCAmelCase__				,    ):
				__SCREAMING_SNAKE_CASE				=					{} if sp_model_kwargs is None else sp_model_kwargs

				# Add extra_ids to the special token list
				super().__init__(
				    bos_token=UpperCamelCase_				,    eos_token=UpperCamelCase_				,    unk_token=UpperCamelCase_				,    pad_token=UpperCamelCase_				,    sep_token=UpperCamelCase_				,    sp_model_kwargs=self.sp_model_kwargs				,    **UpperCamelCase_				,    )

				__SCREAMING_SNAKE_CASE				=					vocab_file

				__SCREAMING_SNAKE_CASE				=					spm.SentencePieceProcessor(**self.sp_model_kwargs)
				self.sp_model.Load(UpperCamelCase_)




		@property
		def       snake_case_	(	self):
				return self.sp_model.get_piece_size()




		def       snake_case_	(	self):
				__SCREAMING_SNAKE_CASE				=					{self.convert_ids_to_tokens(UpperCamelCase_): i for i in range(self.vocab_size)}
				vocab.update(self.added_tokens_encoder)
				return vocab




		def __getstate__(	self):
				__SCREAMING_SNAKE_CASE				=					self.__dict__.copy()
				__SCREAMING_SNAKE_CASE				=					None
				return state




		def __setstate__(	self				,    lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE				=					d

				# for backward compatibility
				if not hasattr(self				,    """sp_model_kwargs"""):
						__SCREAMING_SNAKE_CASE				=					{}

				__SCREAMING_SNAKE_CASE				=					spm.SentencePieceProcessor(**self.sp_model_kwargs)
				self.sp_model.Load(self.vocab_file)




		def       snake_case_	(	self				,    lowerCAmelCase__):
				return self.sp_model.encode(UpperCamelCase_				,    out_type=UpperCamelCase_)




		def       snake_case_	(	self				,    lowerCAmelCase__):
				return self.sp_model.piece_to_id(UpperCamelCase_)




		def       snake_case_	(	self				,    lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE				=					self.sp_model.IdToPiece(UpperCamelCase_)
				return token




		def       snake_case_	(	self				,    lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE				=					[]
				__SCREAMING_SNAKE_CASE				=					""""""
				for token in tokens:
						# make sure that special tokens are not decoded using sentencepiece model
						if token in self.all_special_tokens:
								out_string += self.sp_model.decode(UpperCamelCase_) + token
								__SCREAMING_SNAKE_CASE				=					[]
						else:
								current_sub_tokens.append(UpperCamelCase_)
				out_string += self.sp_model.decode(UpperCamelCase_)
				return out_string.strip()




		def       snake_case_	(	self				,    lowerCAmelCase__				,    lowerCAmelCase__ = None):

				if not os.path.isdir(UpperCamelCase_):
						logger.error(f"Vocabulary path ({save_directory}) should be a directory")
						return
				__SCREAMING_SNAKE_CASE				=					os.path.join(
				    UpperCamelCase_				,    (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])

				if os.path.abspath(self.vocab_file) != os.path.abspath(UpperCamelCase_) and os.path.isfile(self.vocab_file):
						copyfile(self.vocab_file				,    UpperCamelCase_)
				elif not os.path.isfile(self.vocab_file):
						with open(UpperCamelCase_				,    """wb""") as fi:
								__SCREAMING_SNAKE_CASE				=					self.sp_model.serialized_model_proto()
								fi.write(UpperCamelCase_)

				return (out_vocab_file,)












"""simple docstring"""
def 							A						(				__snake_case: int = 3 ,     __snake_case: int = 7 ,     __snake_case: int = 1_0_0_0_0_0_0  )    ->		int:

       """simple docstring"""





       __magic_name__      				=    0
       __magic_name__      				=    1

       for current_denominator in range(1 ,     limit + 1  ):
              __magic_name__      				=    current_denominator * numerator // denominator
              if current_denominator % denominator == 0:
                     current_numerator -= 1
              if current_numerator * max_denominator > current_denominator * max_numerator:
                     __magic_name__      				=    current_numerator
                     __magic_name__      				=    current_denominator
       return max_numerator


if __name__ == "__main__":
 print(solution(numerator=3, denominator=7, limit=1_0_0_0_0_0_0))